{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [
    {
     "file_id": "1z2-whOBA2uBslFo3qVy-JS8P86vVW33j",
     "timestamp": 1669899809979
    },
    {
     "file_id": "1xqdHDL9suDJhDXOdUT9TrR2znFNAxg52",
     "timestamp": 1668049647097
    },
    {
     "file_id": "1S3Hlqt-nNNriGM1QOpuD23tlCn31Ykz_",
     "timestamp": 1651113595545
    }
   ]
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Project 2\n",
    "# Rebalancing Strategy \n",
    "\n",
    "#### 2022-2학기\n",
    "#### Version: 2022-11-11-v1\n",
    "\n",
    "> 분반:\n",
    ">\n",
    "> 학번:\n",
    ">\n",
    "> 학과:\n",
    ">\n",
    "> 이름:\n",
    "\n",
    "### To Do List\n",
    "\n",
    "- rebalancing() 완성 (만약 BTC, ETH 외의 암호화폐를 추가적으로 고려하고 싶은 경우)\n",
    "- 여러 자산 비율을 사용하여 성능 테스트\n",
    "-译：完成rebalancing() (如果想追加考虑BTC、 ETH以外的加密货币)\n",
    "-译：使用多个资产比率进行性能测试"
   ],
   "metadata": {
    "id": "NlY5bYudAnHa"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# 런타임이 종료되면 이 코드셀은 매번 다시 실행해야 합니다. \n",
    "#译：运行时间结束后，这个code cell（代码单元）每次都要重新运行。\n",
    "\n",
    "# pyupbit 설치 （安装pyupbit）\n",
    "!pip install pyupbit"
   ],
   "metadata": {
    "id": "9PuoRkgJ1oJa"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "BT-LddYo1g0w"
   },
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "import pyupbit\n"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# trading_helper 설치 및 버전 확인 （译： trading_helper 安装和版本确认）\n",
    "\n",
    "# !wget https://esohn.be/python/trading_helper.py -O trading_helper.py\n",
    "\n",
    "import trading_helper\n",
    "import importlib\n",
    "importlib.reload(trading_helper)\n",
    "\n",
    "print( f'Trading Helper Version: {trading_helper.TRADING_HELPER_VERSION}'  )"
   ],
   "metadata": {
    "id": "He3RBCBrNloT"
   },
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trading Helper Version: 2022.11.30-v1\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### AUTH_CODE는 반드시 자신의 Auth Code로 변경해야 합니다.\n",
    "#译：AUTH_CODE必须更改为自己的Auth Code。"
   ],
   "metadata": {
    "id": "iGOo80w1qHn9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# ------------------------------------------------------------------------------------------------------\n",
    "# https://esohn.be/account 의 AUTH CODE 입력\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "\n",
    "'''\n",
    "AUTH_CODE = 'f587ea651febd5446efc61d5e3fef123'  -> 아래에 이렇게 auth code를 붙여넣으세요.（请在下面这样粘贴auth code。）\n",
    "'''\n",
    "AUTH_CODE = '2319d129e17e2cd365363ef4ebfb4596'\n",
    "\n",
    "# Do not edit\n",
    "trading_helper.set_auth_code( AUTH_CODE )\n"
   ],
   "metadata": {
    "id": "SKPplX5TI8mf"
   },
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log updated! (2022-2 YCS1009-01 CAI, LUYAO)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 아래 코드셀을 복사 해서 프로젝트 코드 페이지에 붙여넣으세요\n",
    "#译：复制下面的code cell（代码单元）并粘贴到项目code页上\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "cUCfeNR8YEM3"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# ------------------------------------------------------------------------------------------------------\n",
    "# - 코드를 완성했다면 이 코드셀만 복사해서 competition 페이지 Volatility Breakout 탭에 라운드 별 코드 항목으로 붙여넣습니다.\n",
    "# - 연습할 때는 https://esohn.be/competition 페이지의 에디터에서 직접 연습하지 말고 Colab에서 연습하세요. \n",
    "#   (노트북 연습 횟수가 서버에 기록되며 또 노트북 자체에 변경 history가 축척 됩니다.)\n",
    "#译：\n",
    "#完成代码后，只需复制此代码单元（code cell）并将其作为每轮代码条目粘贴到competition页面的“Volatility Breakout”中。\n",
    "#练习的时候不要在https://esohn.be/competition页面的编辑, 请在Colab里练习\n",
    "#（笔记本电脑练习次数将被记录在服务器上，笔记本电脑本身也会积累变更history。）\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# Given Code Version 2022.11.10-v1\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "# rebalancing() 암호화폐 포트폴리오 리밸런싱 함수 译：rebalancing() Cryptocurrency portfolio rebalancing函数\n",
    "'''\n",
    "* ratio_  자산배분 비율 (총 합은 반드시 1.0이어야 함)\n",
    "  투자하지 않는 암호화폐는 0으로 남겨둠\n",
    "译：ratio_ 资产配置比率( 总计必须为 1.0)\n",
    "  不投资的Cryptocurrency保留为0\n",
    "  \n",
    "  예) 원화 10%, 비트코인(BTC) 50%, 이더리움(ETH) 40%  （例如,韩元10%、比特币(BTC)50%、Ethereum(ETH)）\n",
    "      ratio_krw = 0.1\n",
    "      ratio_btc = 0.5\n",
    "      ratio_eth = 0.4\n",
    "      ratio_doge = 0.0\n",
    "      ratio_ada = 0.0\n",
    "\n",
    "  예) 원화 70%, 비트코인(BTC) 10%, 이더리움(ETH) 10%, 도지코인(DOGE) 5%, 에이다(ADA) 5%  （例如,韩元70%、比特币(BTC)10%、Ethereum(ETH)10%、Dogecoin(DOGE)5%、Ada(ADA)5%）\n",
    "      ratio_krw = 0.7\n",
    "      ratio_btc = 0.1\n",
    "      ratio_eth = 0.1\n",
    "      ratio_doge = 0.05\n",
    "      ratio_ada = 0.05\n",
    "\n",
    "* threshold - 최소한으로 요구되는 총 자산 대비 변동 비율 (%) （* threshold - 最低要求总资产与变动比率(%)）\n",
    "\n",
    "  기본값 0: 조금이라도 변동될 때 매매 （基本值 0: 稍有变动时买卖）\n",
    "  예) 0.001: 전체 자산의 0.1% 변동시 매매, 0.005: 전체 자산의 0.5% 변동시 매매 （例) 0.001:全体资产的0.1%变动时买卖,0.005:全体资产的0.5%变动时买卖）\n",
    "\n",
    "\n",
    "* fee - 수수료, 기본값 0.001 (변경 없음)  （fee - 手续费， 默认值/基本值 0.001 (无变化)）\n",
    "\n",
    "* init_krw - 초기 투자금액, 기본값 100000000 (변경 없음) （init_krw - 初始投资金额， 默认值/基本值为 10000000 (无变化)）\n",
    "'''\n",
    "\n",
    "# Qualification 라운드 에서는 지정된 암호화폐 비율과 threshold 0.0을 사용해야 하며 （Qualification回合中必须使用指定的Cryptocurrency比率和threshold 0.0）\n",
    "# 그 외의 라운드에서는 암포화폐 비율과 threshold를 자유롭게 사용할 수 있음 （在其他回合中，可以自由使用Cryptocurrency比率和threshold。）\n",
    "\n",
    "# ------------------------------------------------\n",
    "# EDITABLE (1)\n",
    "#\n",
    "DEFAULT_RATIO_KRW = 1.0\n",
    "DEFAULT_RATIO_BTC = 0.0\n",
    "DEFAULT_RATIO_ETH = 0.0\n",
    "DEFAULT_RATIO_DOGE = 0.0\n",
    "DEFAULT_RATIO_ADA = 0.0\n",
    "\n",
    "DEFAULT_THRESHOLD = 0.0 # Qualification 라운드 에서는 이 값을 0.0으로 （在DEFAULT_THRESHOLD=0.0 # Qualification回合中，将此值设为0.0）\n",
    "# ------------------------------------------------\n",
    "\n",
    "def rebalancing( df, ratio_krw=DEFAULT_RATIO_KRW, ratio_btc=DEFAULT_RATIO_BTC, ratio_eth=DEFAULT_RATIO_ETH, ratio_doge=DEFAULT_RATIO_DOGE, ratio_ada=DEFAULT_RATIO_ADA, threshold=DEFAULT_THRESHOLD, fee=0.001, init_krw=100000000 ):\n",
    "\n",
    "    # -------------------------------------------------------------------------------\n",
    "    # DO NOT EDIT\n",
    "    #\n",
    "    ratio_sum = ratio_krw + ratio_btc + ratio_eth + ratio_doge + ratio_ada\n",
    "    if abs(ratio_sum - 1.0) > 0.0001:\n",
    "        print(f'The sum of the ratios must be 1. ({ratio_sum})')\n",
    "        return\n",
    "    # -------------------------------------------------------------------------------\n",
    "\n",
    "    row_index = 0\n",
    "\n",
    "    # 데이터프레임의 모든 행에 대해서 반복 （重复数据帧中的所有行）\n",
    "    for row_index in range( len( df ) ):\n",
    "\n",
    "        # 현재 데이터: cur_data  （现在的数据: cur_data）\n",
    "        cur_data = df.loc[ row_index, : ].copy()\n",
    "        # 이전 데이터: prev_data （以前的数据：prev_data）\n",
    "        if row_index > 0:\n",
    "            prev_data = df.loc[ row_index-1, : ].copy()\n",
    "\n",
    "\n",
    "\n",
    "        #------------------------\n",
    "        # Balance 계산 (bal_)。（计算 Balance (bal_)）\n",
    "        # 하루 전의 계산 후 잔고(bal2_)를 현재의 계산 전 잔고로 가져옴(bal_) （将前一天的计算后余额(bal2_) 导入当前计算前余额(bal_)）\n",
    "        if row_index == 0:\n",
    "            # 첫째 날이라면 잔고는 krw만 존재 （如果是第一天，余额就只有krw存在。） \n",
    "            cur_data[ 'bal_krw' ] = init_krw\n",
    "        else:\n",
    "            # 둘째 날 부터는 이전 날 계산 후 잔고(bal2_)를 현재 잔고(bal_)로 대입 （从第二天开始，将前一天计算后的余额(bal2_)代入当前余额(bal_)）\n",
    "            cur_data[ 'bal_krw' ] = prev_data[ 'bal2_krw' ]\n",
    "            cur_data[ 'bal_btc' ] = prev_data[ 'bal2_btc' ]\n",
    "            cur_data[ 'bal_eth' ] = prev_data[ 'bal2_eth' ]\n",
    "            cur_data[ 'bal_doge' ] = prev_data[ 'bal2_doge' ]\n",
    "            cur_data[ 'bal_ada' ] = prev_data[ 'bal2_ada' ]\n",
    "            \n",
    "\n",
    "        #------------------------\n",
    "        # Total Value 계산 (value) （计算总价值）\n",
    "        # 전체 자산의 가치를 모두 합산  （全部资产的价值总和）\n",
    "        cur_data[ 'value' ] = cur_data[ 'bal_krw' ]\n",
    "        cur_data[ 'value' ] += cur_data[ 'price_btc' ] * cur_data[ 'bal_btc' ]\n",
    "        cur_data[ 'value' ] += cur_data[ 'price_eth' ] * cur_data[ 'bal_eth' ]\n",
    "        cur_data[ 'value' ] += 0 # EDITABLE (4)\n",
    "        cur_data[ 'value' ] += 0 # EDITABLE (5)                                \n",
    "                                \n",
    "\n",
    "        #------------------------\n",
    "        # Target 계산 (target_)。（计算目标( target_)）\n",
    "        # 비율에 따라 다시 조정되어야 할 목표 잔고 계산 （按比例重新调整的目标余额计算）\n",
    "        cur_data[ 'target_btc' ] = ratio_btc * cur_data[ 'value' ] / cur_data[ 'price_btc' ]\n",
    "        cur_data[ 'target_eth' ] = ratio_eth * cur_data[ 'value' ] / cur_data[ 'price_eth' ]\n",
    "        cur_data[ 'target_doge' ] = 0    # EDITABLE (6)\n",
    "        cur_data[ 'target_ada' ] = 0     # EDITABLE (7)\n",
    "\n",
    "\n",
    "        #------------------------\n",
    "        # Trading Amount 계산 (trade_)  （交易额计算(trade_)）\n",
    "        # Target 잔고를 위해 매매해야 할 양을 계산 （Target 计算余额所需的交易量）\n",
    "        cur_data[ 'trade_btc' ] = cur_data[ 'target_btc' ] - cur_data[ 'bal_btc' ]\n",
    "        cur_data[ 'trade_eth' ] = cur_data[ 'target_eth' ] - cur_data[ 'bal_eth' ]\n",
    "        cur_data[ 'trade_doge' ] = 0    # EDITABLE (8)\n",
    "        cur_data[ 'trade_ada' ] = 0     # EDITABLE (9)\n",
    "\n",
    "\n",
    "        #------------------------\n",
    "        # 매매해야 할 양의 절대값이 총 자산의 일정 %를 넘지 않으면 매매하지 않음 （待购数量的绝对值不超过总资产的一定比例时，不进行买卖。）\n",
    "        th_value = threshold * cur_data[ 'value' ]\n",
    "        amount_btc = abs( cur_data[ 'trade_btc' ] * cur_data[ 'price_btc' ] )\n",
    "        amount_eth = abs( cur_data[ 'trade_eth' ] * cur_data[ 'price_eth' ] )\n",
    "        amount_doge = 0    # EDITABLE (10)\n",
    "        amount_ada = 0     # EDITABLE (11)\n",
    "\n",
    "        #--------------------------------------------------------------------------------------------------------------------\n",
    "        # DO NOT EDIT\n",
    "        # 계산 후 잔고 준비 （结账后余额准备）\n",
    "        cur_data[ 'bal2_krw' ] = cur_data[ 'bal_krw' ]\n",
    "        cur_data[ 'bal2_btc' ] = cur_data[ 'bal_btc' ]\n",
    "        cur_data[ 'bal2_eth' ] = cur_data[ 'bal_eth' ]\n",
    "        cur_data[ 'bal2_doge' ] = cur_data[ 'bal_doge' ]\n",
    "        cur_data[ 'bal2_ada' ] = cur_data[ 'bal_ada' ]\n",
    "\n",
    "        if amount_btc >= th_value:\n",
    "            # BTC 잔고 변경  （更改 BTC 余额）\n",
    "            cur_data[ 'bal2_btc' ] += cur_data[ 'trade_btc' ]\n",
    "            # krw 잔고 변경 (BTC 매매 반영 + 수수료 차감) （更改 krw 余额( 反映 BTC 交易 + 扣除手续费)）\n",
    "            cur_data[ 'bal2_krw' ] -= cur_data[ 'trade_btc' ] * cur_data[ 'price_btc' ] + amount_btc * fee\n",
    "\n",
    "        if amount_eth >= th_value:\n",
    "            # ETH 잔고 변경  （ETH余额变更）\n",
    "            cur_data[ 'bal2_eth' ] += cur_data[ 'trade_eth' ]\n",
    "            # krw 잔고 변경 (ETH 매매 반영 + 수수료 차감) （更改 krw 余额( 反映 ETH 交易 + 扣除手续费)）\n",
    "            cur_data[ 'bal2_krw' ] -= cur_data[ 'trade_eth' ] * cur_data[ 'price_eth' ] + amount_eth * fee\n",
    "\n",
    "        if amount_doge >= th_value:\n",
    "            # DOGE 잔고 변경  （更改 DOGE 余额）\n",
    "            cur_data[ 'bal2_doge' ] += cur_data[ 'trade_doge' ]\n",
    "            # krw 잔고 변경 (DOGE 매매 반영 + 수수료 차감) （更改 krw 余额( 反映 DOGE 交易 + 扣除手续费)）\n",
    "            cur_data[ 'bal2_krw' ] -= cur_data[ 'trade_doge' ] * cur_data[ 'price_doge' ] + amount_doge * fee\n",
    "\n",
    "        if amount_ada >= th_value:\n",
    "            # ADA 잔고 변경  （更改 ADA 余额）\n",
    "            cur_data[ 'bal2_ada' ] += cur_data[ 'trade_ada' ]\n",
    "            # krw 잔고 변경 (ADA 매매 반영 + 수수료 차감) （更改 krw 余额( 反映 ADA 买卖 + 扣除手续费)）\n",
    "            cur_data[ 'bal2_krw' ] -= cur_data[ 'trade_ada' ] * cur_data[ 'price_ada' ] + amount_ada * fee\n",
    "\n",
    "\n",
    "        #--------------------------------------------------------------------------------------------------------------------\n",
    "        # DO NOT EDIT\n",
    "        #\n",
    "        df.loc[ row_index, : ] = cur_data\n",
    "\n",
    "        row_index += 1\n",
    "        #--------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "-ZdtSo-7zhgC"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# 가격정보 가져오기（获取价格信息）\n",
    "\n",
    "from_date = '20210401'\n",
    "to_date = '20221101'\n",
    "\n",
    "df = trading_helper.get_multiple(from_date, to_date)\n",
    "\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "id": "VKLTtAblVAzL"
   },
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0.2  Unnamed: 0.1  Unnamed: 0                      index  \\\n0             0             0           0  2021-04-01 09:00:00+09:00   \n1             1             1           1  2021-04-02 09:00:00+09:00   \n2             2             2           2  2021-04-03 09:00:00+09:00   \n3             3             3           3  2021-04-04 09:00:00+09:00   \n4             4             4           4  2021-04-05 09:00:00+09:00   \n\n    price_btc  price_eth  price_doge  price_ada  bal_krw  bal_btc  ...  \\\n0  71480000.0  2333000.0        65.5     1455.0        0        0  ...   \n1  72749000.0  2436000.0        77.3     1465.0        0        0  ...   \n2  73934000.0  2679000.0        72.3     1495.0        0        0  ...   \n3  72187000.0  2544000.0        70.4     1470.0        0        0  ...   \n4  73908000.0  2636000.0        73.2     1510.0        0        0  ...   \n\n   bal2_ada  target_btc  target_eth  target_doge  target_ada  trade_btc  \\\n0         0           0           0            0           0          0   \n1         0           0           0            0           0          0   \n2         0           0           0            0           0          0   \n3         0           0           0            0           0          0   \n4         0           0           0            0           0          0   \n\n   trade_eth  trade_doge  trade_ada  value  \n0          0           0          0      0  \n1          0           0          0      0  \n2          0           0          0      0  \n3          0           0          0      0  \n4          0           0          0      0  \n\n[5 rows x 27 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0.2</th>\n      <th>Unnamed: 0.1</th>\n      <th>Unnamed: 0</th>\n      <th>index</th>\n      <th>price_btc</th>\n      <th>price_eth</th>\n      <th>price_doge</th>\n      <th>price_ada</th>\n      <th>bal_krw</th>\n      <th>bal_btc</th>\n      <th>...</th>\n      <th>bal2_ada</th>\n      <th>target_btc</th>\n      <th>target_eth</th>\n      <th>target_doge</th>\n      <th>target_ada</th>\n      <th>trade_btc</th>\n      <th>trade_eth</th>\n      <th>trade_doge</th>\n      <th>trade_ada</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2021-04-01 09:00:00+09:00</td>\n      <td>71480000.0</td>\n      <td>2333000.0</td>\n      <td>65.5</td>\n      <td>1455.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2021-04-02 09:00:00+09:00</td>\n      <td>72749000.0</td>\n      <td>2436000.0</td>\n      <td>77.3</td>\n      <td>1465.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2021-04-03 09:00:00+09:00</td>\n      <td>73934000.0</td>\n      <td>2679000.0</td>\n      <td>72.3</td>\n      <td>1495.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2021-04-04 09:00:00+09:00</td>\n      <td>72187000.0</td>\n      <td>2544000.0</td>\n      <td>70.4</td>\n      <td>1470.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>2021-04-05 09:00:00+09:00</td>\n      <td>73908000.0</td>\n      <td>2636000.0</td>\n      <td>73.2</td>\n      <td>1510.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 하나씩 비율을 조절하며 HPR, MDD 를 확인해 보는 방법 \n",
    "#（逐一调整比例，确认HPR、MDD的方法）"
   ],
   "metadata": {
    "id": "7eSOfPDyYQuq"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# 비트코인만 100% （只有比特币是100%）\n",
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=1.0, ratio_eth=0.0, ratio_doge=0.0, ratio_ada=0.0, threshold= 0.0001 )\n",
    "\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n"
   ],
   "metadata": {
    "id": "99vdq_Sl_bch"
   },
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HPR=-59.3 MDD=69.2\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# 비트코인 60% 이더리움 40% （60%的比特币是Ethereum 40%）\n",
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=0.6, ratio_eth=0.4, ratio_doge=0.0, ratio_ada=0.0, threshold= 0.0001 )\n",
    "\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n"
   ],
   "metadata": {
    "id": "ya-M1T8lD4re"
   },
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HPR=-39.7 MDD=72.5\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 비트코인, 이더리움, 도지코인, 에이다 그냥 가지고 있을 때와 리밸런싱 전략 4개의 HPR, MDD 비교\n",
    "# 比特币、Ethereum、Dogecoin、Ada 仅拥有和rebalancing战略 4 种HPR、MDD比较"
   ],
   "metadata": {
    "id": "AKYgDNdJYbvF"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=1.0, ratio_eth=0.0, ratio_doge=0.0, ratio_ada=0.0 )\n",
    "s_btc = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=0.0, ratio_eth=1.0, ratio_doge=0.0, ratio_ada=0.0 )\n",
    "s_eth = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=0.0, ratio_eth=0.0, ratio_doge=1.0, ratio_ada=0.0 )\n",
    "s_doge = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.0, ratio_btc=0.0, ratio_eth=0.0, ratio_doge=0.0, ratio_ada=1.0 )\n",
    "s_ada = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.7, ratio_btc=0.1, ratio_eth=0.1, ratio_doge=0.05, ratio_ada=0.05 )\n",
    "s1 = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.6, ratio_btc=0.2, ratio_eth=0.1, ratio_doge=0.05, ratio_ada=0.05 )\n",
    "s2 = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.3, ratio_btc=0.2, ratio_eth=0.3, ratio_doge=0.1, ratio_ada=0.1 )\n",
    "s3 = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n",
    "\n",
    "rebalancing( df, ratio_krw=0.2, ratio_btc=0.2, ratio_eth=0.2, ratio_doge=0.2, ratio_ada=0.2 )\n",
    "s4 = df['value'].copy()\n",
    "hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n"
   ],
   "metadata": {
    "id": "9l4pB45uZVzq"
   },
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HPR=-59.3 MDD=69.2\n",
      "HPR=-3.1 MDD=77.4\n",
      "HPR=0.0 MDD=0.0\n",
      "HPR=0.0 MDD=0.0\n",
      "HPR=-2.6 MDD=21.7\n",
      "HPR=-9.8 MDD=30.1\n",
      "HPR=-6.7 MDD=47.5\n",
      "HPR=-7.8 MDD=39.3\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "for i in np.arange(0, 0.6, 0.1):\n",
    "    x = i\n",
    "    y = 0.5 - i\n",
    "\n",
    "    print( x, y, end=' ' )\n",
    "\n",
    "    rebalancing( df, ratio_krw= 0.5, ratio_btc=0, ratio_eth=0, ratio_doge=x, ratio_ada=y )\n",
    "    s4 = df['value'].copy()\n",
    "    hpr, mdd = trading_helper.check_performance_rebal(df)\n",
    "    print( f'HPR={hpr*100:.1f} MDD={mdd*100:.1f}' )\n"
   ],
   "metadata": {
    "id": "C_swfk0vnV1v"
   },
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.5 HPR=0.0 MDD=0.0\n",
      "0.1 0.4 HPR=0.0 MDD=0.0\n",
      "0.2 0.3 HPR=0.0 MDD=0.0\n",
      "0.30000000000000004 0.19999999999999996 HPR=0.0 MDD=0.0\n",
      "0.4 0.09999999999999998 HPR=0.0 MDD=0.0\n",
      "0.5 0.0 HPR=0.0 MDD=0.0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 그래프 그려보기\n",
    "### 绘制图表"
   ],
   "metadata": {
    "id": "zlNi9iLkYumn"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "df2 = pd.DataFrame()\n",
    "# df2['BTC'] = s_btc\n",
    "# df2['ETH'] = s_eth\n",
    "df2['DOGE'] = s_doge\n",
    "df2['ADA'] = s_ada\n",
    "# df2['strategy1'] = s1\n",
    "df2['strategy2'] = s2\n",
    "# df2['strategy3'] = s3\n",
    "# df2['strategy4'] = s4\n",
    "\n",
    "df2.plot( figsize=(15,3) )"
   ],
   "metadata": {
    "id": "939Vkn6o_TfX"
   },
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: >"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x300 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  }
 ]
}
